초록

Data distribution across trusted agents by a distributor is difficult to analyze and manage as there is always the danger of misappropriation by one or more number of agents. A prior approach such as Watermarks that alters the data is of no use. In case of a leak the data is found at an unauthorized place (e.g., on the web or somebody’s laptop). Nothing can be done in former’s case. In a latter scenario data allocation strategies like injecting “realistic but fake” data records to the original sensitive data were developed such that these fake tuples can be used to identify the leaker among the trusted agents. Also guilt assessment algorithms were proposed for distributing objects to agents, in a way that improves the chances of identifying a leaker. These strategies have limited functionality as they function with an assumption that there are a fixed set of agents with requests known in advance. We propose to extend the functionality of these techniques to handle more number of agents using staged event-driven architecture (SEDA). Using this model a distributor can assess the likelihood of a leaker among more number of trusted agents in the aftermath of a leak.